149 research outputs found
Joint Communication and Computation in Hybrid Cloud/Mobile Edge Computing Networks
Facing a vast amount of connections, huge performance demands, and the need
for reliable connectivity, the sixth generation of communication networks (6G)
is envisioned to implement disruptive technologies that jointly spur
connectivity, performance, and reliability. In this context, this paper
proposes, and evaluates the benefit of, a hybrid central cloud (CC) computing
and mobile edge computing (MEC) platform, especially introduced to balance the
network resources required for joint computation and communication. Consider a
hybrid cloud and MEC system, where several power-hungry multi-antenna unmanned
aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC
connectivity and relieve part of its computation burden. While the
multi-antenna base stations are connected to the cloud via capacity-limited
fronthaul links, the UAVs serve the cell-edge users with limited power and
computational capabilities. The paper then considers the problem of maximizing
the weighted network sum-rate subject to per-user delay, computational
capacity, and power constraints, so as to determine the beamforming vectors and
computation allocations. Such intricate non-convex optimization problem is
tackled using an iterative algorithm that relies on -norm relaxation,
successive convex approximation, and fractional programming, and has the
compelling ability to be implemented in a distributed fashion across the
multiple UAVs and the CC. The paper results illustrate the numerical prospects
of the proposed algorithm for enabling joint communication and computation, and
highlight the appreciable improvements of data processing delays and
throughputs as compared to conventional system strategies.Comment: 7 pages, 3 figures, 2 tables, copyright IEEE, the paper has been
accepted for publication at the IEEE Global Communications Conference
(Globecom) Workshops, 978-1-6654-5975-4/22 c 2022 IEE
Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps
The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
Mobile communications have been undergoing a generational change every ten
years or so. However, the time difference between the so-called "G's" is also
decreasing. While fifth-generation (5G) systems are becoming a commercial
reality, there is already significant interest in systems beyond 5G, which we
refer to as the sixth-generation (6G) of wireless systems. In contrast to the
already published papers on the topic, we take a top-down approach to 6G. We
present a holistic discussion of 6G systems beginning with lifestyle and
societal changes driving the need for next generation networks. This is
followed by a discussion into the technical requirements needed to enable 6G
applications, based on which we dissect key challenges, as well as
possibilities for practically realizable system solutions across all layers of
the Open Systems Interconnection stack. Since many of the 6G applications will
need access to an order-of-magnitude more spectrum, utilization of frequencies
between 100 GHz and 1 THz becomes of paramount importance. As such, the 6G
eco-system will feature a diverse range of frequency bands, ranging from below
6 GHz up to 1 THz. We comprehensively characterize the limitations that must be
overcome to realize working systems in these bands; and provide a unique
perspective on the physical, as well as higher layer challenges relating to the
design of next generation core networks, new modulation and coding methods,
novel multiple access techniques, antenna arrays, wave propagation,
radio-frequency transceiver design, as well as real-time signal processing. We
rigorously discuss the fundamental changes required in the core networks of the
future that serves as a major source of latency for time-sensitive
applications. While evaluating the strengths and weaknesses of key 6G
technologies, we differentiate what may be achievable over the next decade,
relative to what is possible.Comment: Accepted for Publication into the Proceedings of the IEEE; 32 pages,
10 figures, 5 table
NFV orchestration in edge and fog scenarios
Mención Internacional en el título de doctorLas infraestructuras de red actuales soportan una
variedad diversa de servicios como video bajo demanda,
video conferencias, redes sociales, sistemas
de educación, o servicios de almacenamiento de
fotografías. Gran parte de la población mundial ha
comenzado a utilizar estos servicios, y los utilizan
diariamente. Proveedores de Cloud y operadores
de infraestructuras de red albergan el tráfico de
red generado por estos servicios, y sus tareas de
gestión no solo implican realizar el enrutamiento
del tráfico, sino también el procesado del tráfico de
servicios de red. Tradicionalmente, el procesado
del tráfico ha sido realizado mediante aplicaciones/
programas desplegados en servidores que estaban
dedicados en exclusiva a tareas concretas
como la inspección de paquetes. Sin embargo, en
los últimos anos los servicios de red se han virtualizado
y esto ha dado lugar al paradigma de
virtualización de funciones de red (Network Function
Virtualization (NFV) siguiendo las siglas en
ingles), en el que las funciones de red de un servicio
se ejecutan en contenedores o máquinas virtuales
desacopladas de la infraestructura hardware. Como
resultado, el procesado de tráfico se ha ido
haciendo más flexible gracias al laxo acople del
software y hardware, y a la posibilidad de compartir
funciones de red típicas, como firewalls, entre
los distintos servicios de red.
NFV facilita la automatización de operaciones
de red, ya que tareas como el escalado, o la migración
son típicamente llevadas a cabo mediante
un conjunto de comandos previamente definidos
por la tecnología de virtualización pertinente, bien
mediante contenedores o máquinas virtuales. De
todos modos, sigue siendo necesario decidir el en rutamiento y procesado del tráfico de cada servicio
de red. En otras palabras, que servidores tienen
que encargarse del procesado del tráfico, y que
enlaces de la red tienen que utilizarse para que las
peticiones de los usuarios lleguen a los servidores
finales, es decir, el conocido como embedding problem.
Bajo el paraguas del paradigma NFV, a este
problema se le conoce en inglés como Virtual Network
Embedding (VNE), y esta tesis utiliza el termino
“NFV orchestration algorithm” para referirse
a los algoritmos que resuelven este problema. El
problema del VNE es NP-hard, lo cual significa
que que es imposible encontrar una solución optima
en un tiempo polinómico, independientemente
del tamaño de la red. Como consecuencia, la comunidad
investigadora y de telecomunicaciones
utilizan heurísticos que encuentran soluciones de
manera más rápida que productos para la resolución
de problemas de optimización.
Tradicionalmente, los “NFV orchestration algorithms”
han intentado minimizar los costes de
despliegue derivados de las soluciones asociadas.
Por ejemplo, estos algoritmos intentan no consumir
el ancho de banda de la red, y usar rutas cortas
para no utilizar tantos recursos. Además, una tendencia
reciente ha llevado a la comunidad investigadora
a utilizar algoritmos que minimizan el
consumo energético de los servicios desplegados,
bien mediante la elección de dispositivos con un
consumo energético más eficiente, o mediante el
apagado de dispositivos de red en desuso. Típicamente,
las restricciones de los problemas de VNE se
han resumido en un conjunto de restricciones asociadas
al uso de recursos y consumo energético, y las
soluciones se diferenciaban por la función objetivo
utilizada. Pero eso era antes de la 5a generación de
redes móviles (5G) se considerase en el problema
de VNE. Con la aparición del 5G, nuevos servicios
de red y casos de uso entraron en escena. Los estándares
hablaban de comunicaciones ultra rápidas
y fiables (Ultra-Reliable and Low Latency Communications
(URLLC) usando las siglas en inglés) con
latencias por debajo de unos pocos milisegundos y
fiabilidades del 99.999%, una banda ancha mejorada
(enhanced Mobile Broadband (eMBB) usando
las siglas en inglés) con notorios incrementos en
el flujo de datos, e incluso la consideración de comunicaciones
masivas entre maquinas (Massive
Machine-Type Communications (mMTC) usando
las siglas en inglés) entre dispositivos IoT. Es más,
paradigmas como edge y fog computing se incorporaron a la tecnología 5G, e introducían la idea
de tener dispositivos de computo más cercanos al
usuario final. Como resultado, el problema del VNE
tenía que incorporar los nuevos requisitos como
restricciones a tener en cuenta, y toda solución
debía satisfacer bajas latencias, alta fiabilidad, y
mayores tasas de transmisión.
Esta tesis estudia el problema des VNE, y propone
algunos heurísticos que lidian con las restricciones
asociadas a servicios 5G en escenarios
edge y fog, es decir, las soluciones propuestas se
encargan de asignar funciones virtuales de red a
servidores, y deciden el enrutamiento del trafico
en las infraestructuras 5G con dispositivos edge y
fog. Para evaluar el rendimiento de las soluciones
propuestas, esta tesis estudia en primer lugar la
generación de grafos que representan redes 5G.
Los mecanismos propuestos para la generación de
grafos sirven para representar distintos escenarios
5G. En particular, escenarios de federación en
los que varios dominios comparten recursos entre
ellos. Los grafos generados también representan
servidores en el edge, así como dispositivos fog con
una batería limitada. Además, estos grafos tienen
en cuenta los requisitos de estándares, y la demanda
que se espera en las redes 5G. La generación de
grafos propuesta sirve para representar escenarios
federación en los que varios dominios comparten
recursos entre ellos, y redes 5G con servidores edge,
así como dispositivos fog estáticos o móviles con
una batería limitada. Los grafos generados para
infraestructuras 5G tienen en cuenta los requisitos
de estándares, y la demanda de red que se espera
en las redes 5G. Además, los grafos son diferentes
en función de la densidad de población, y el área
de estudio, es decir, si es una zona industrial, una
autopista, o una zona urbana.
Tras detallar la generación de grafos que representan
redes 5G, esta tesis propone algoritmos de
orquestación NFV para resolver con el problema
del VNE. Primero, se centra en escenarios federados
en los que los servicios de red se tienen que
asignar no solo a la infraestructura de un dominio,
sino a los recursos compartidos en la federación
de dominios. Dos problemas diferentes han sido estudiados,
uno es el problema del VNE propiamente
dicho sobre una infraestructura federada, y el otro
es la delegación de servicios de red. Es decir, si
un servicio de red se debe desplegar localmente
en un dominio, o en los recursos compartidos por
la federación de dominios; a sabiendas de que el último caso supone el pago de cuotas por parte del
dominio local a cambio del despliegue del servicio
de red. En segundo lugar, esta tesis propone
OKpi, un algoritmo de orquestación NFV para conseguir
la calidad de servicio de las distintas slices
de las redes 5G. Conceptualmente, el slicing consiste
en partir la red de modo que cada servicio
de red sea tratado de modo diferente dependiendo
del trozo al que pertenezca. Por ejemplo, una
slice de eHealth reservara los recursos de red necesarios
para conseguir bajas latencias en servicios
como operaciones quirúrgicas realizadas de manera
remota. Cada trozo (slice) está destinado a
unos servicios específicos con unos requisitos muy
concretos, como alta fiabilidad, restricciones de
localización, o latencias de un milisegundo. OKpi
es un algoritmo de orquestación NFV que consigue
satisfacer los requisitos de servicios de red en los
distintos trozos, o slices de la red. Tras presentar
OKpi, la tesis resuelve el problema del VNE en redes
5G con dispositivos fog estáticos y móviles. El
algoritmo de orquestación NFV presentado tiene
en cuenta las limitaciones de recursos de computo
de los dispositivos fog, además de los problemas
de falta de cobertura derivados de la movilidad de
los dispositivos.
Para concluir, esta tesis estudia el escalado
de servicios vehiculares Vehicle-to-Network (V2N),
que requieren de bajas latencias para servicios como
la prevención de choques, avisos de posibles
riesgos, y conducción remota. Para estos servicios,
los atascos y congestiones en la carretera pueden
causar el incumplimiento de los requisitos de latencia.
Por tanto, es necesario anticiparse a esas
circunstancias usando técnicas de series temporales
que permiten saber el tráfico inminente en los
siguientes minutos u horas, para así poder escalar
el servicio V2N adecuadamente.Current network infrastructures handle a diverse
range of network services such as video
on demand services, video-conferences, social
networks, educational systems, or photo
storage services. These services have been
embraced by a significant amount of the
world population, and are used on a daily basis.
Cloud providers and Network operators’
infrastructures accommodate the traffic rates
that the aforementioned services generate, and
their management tasks do not only involve
the traffic steering, but also the processing of
the network services’ traffic. Traditionally,
the traffic processing has been assessed via
applications/programs deployed on servers
that were exclusively dedicated to a specific
task as packet inspection. However, in recent
years network services have stated to be
virtualized and this has led to the Network
Function Virtualization (Network Function
Virtualization (NFV)) paradigm, in which the
network functions of a service run on containers
or virtual machines that are decoupled
from the hardware infrastructure. As a result,
the traffic processing has become more flexible
because of the loose coupling between
software and hardware, and the possibility
of sharing common network functions, as
firewalls, across multiple network services.
NFV eases the automation of network operations,
since scaling and migrations tasks
are typically performed by a set of commands
predefined by the virtualization technology,
either containers or virtual machines. However,
it is still necessary to decide the traffic steering and processing of every network
service. In other words, which servers will
hold the traffic processing, and which are the
network links to be traversed so the users’ requests
reach the final servers, i.e., the network
embedding problem. Under the umbrella of
NFV, this problem is known as Virtual Network
Embedding (VNE), and this thesis refers
as “NFV orchestration algorithms” to those
algorithms solving such a problem. The VNE
problem is a NP-hard, meaning that it is impossible
to find optimal solutions in polynomial
time, no matter the network size. As a
consequence, the research and telecommunications
community rely on heuristics that find
solutions quicker than a commodity optimization
solver.
Traditionally, NFV orchestration algorithms
have tried to minimize the deployment
costs derived from their solutions. For example,
they try to not exhaust the network
bandwidth, and use short paths to use less
network resources. Additionally, a recent
tendency led the research community towards
algorithms that minimize the energy consumption
of the deployed services, either
by selecting more energy efficient devices
or by turning off those network devices that
remained unused. VNE problem constraints
were typically summarized in a set of resources/energy constraints, and the solutions
differed on which objectives functions were
aimed for. But that was before 5th generation
of mobile networks (5G) were considered
in the VNE problem. With the appearance
of 5G, new network services and use cases
started to emerge. The standards talked about
Ultra Reliable Low Latency Communication
(Ultra-Reliable and Low Latency Communications
(URLLC)) with latencies below few
milliseconds and 99.999% reliability, an enhanced
mobile broadband (enhanced Mobile
Broadband (eMBB)) with significant data
rate increases, and even the consideration
of massive machine-type communications
(Massive Machine-Type Communications
(mMTC)) among Internet of Things (IoT) devices.
Moreover, paradigms such as edge and
fog computing blended with the 5G technology
to introduce the idea of having computing
devices closer to the end users. As a result, the VNE problem had to incorporate the new
requirements as constraints to be taken into
account, and every solution should either
satisfy low latencies, high reliability, or larger
data rates.
This thesis studies the VNE problem, and
proposes some heuristics tackling the constraints
related to 5G services in Edge and
fog scenarios, that is, the proposed solutions
assess the assignment of Virtual Network
Functions to resources, and the traffic steering
across 5G infrastructures that have Edge and
Fog devices. To evaluate the performance
of the proposed solutions, the thesis studies
first the generation of graphs that represent
5G networks. The proposed mechanisms to
generate graphs serve to represent diverse 5G
scenarios. In particular federation scenarios
in which several domains share resources
among themselves. The generated graphs
also represent edge servers, so as fog devices
with limited battery capacity. Additionally,
these graphs take into account the standard
requirements, and the expected demand for
5G networks. Moreover, the graphs differ depending
on the density of population, and the
area of study, i.e., whether it is an industrial
area, a highway, or an urban area.
After detailing the generation of graphs
representing the 5G networks, this thesis proposes
several NFV orchestration algorithms
to tackle the VNE problem. First, it focuses
on federation scenarios in which network services
should be assigned not only to a single
domain infrastructure, but also to the shared
resources of the federation of domains. Two
different problems are studied, one being the
VNE itself over a federated infrastructure, and
the other the delegation of network services.
That is, whether a network service should be
deployed in a local domain, or in the pool
of resources of the federation domain; knowing
that the latter charges the local domain
for hosting the network service. Second, the
thesis proposes OKpi, a NFV orchestration
algorithm to meet 5G network slices quality
of service. Conceptually, network slicing consists
in splitting the network so network services
are treated differently based on the slice
they belong to. For example, an eHealth network
slice will allocate the network resources necessary to meet low latencies for network
services such as remote surgery. Each network
slice is devoted to specific services with
very concrete requirements, as high reliability,
location constraints, or 1ms latencies. OKpi is
a NFV orchestration algorithm that meets the
network service requirements among different
slices. It is based on a multi-constrained
shortest path heuristic, and its solutions satisfy
latency, reliability, and location constraints.
After presenting OKpi, the thesis tackles the
VNE problem in 5G networks with static/moving
fog devices. The presented NFV orchestration
algorithm takes into account the limited
computing resources of fog devices, as well
as the out-of-coverage problems derived from
the devices’ mobility.
To conclude, this thesis studies the scaling
of Vehicle-to-Network (V2N) services, which
require low latencies for network services as
collision avoidance, hazard warning, and remote
driving. For these services, the presence
of traffic jams, or high vehicular traffic congestion
lead to the violation of latency requirements.
Hence, it is necessary to anticipate to
such circumstances by using time-series techniques
that allow to derive the incoming vehicular
traffic flow in the next minutes or hours,
so as to scale the V2N service accordingly.The 5G Exchange (5GEx) project (2015-2018) was an EU-funded project (H2020-ICT-2014-2 grant agreement 671636).
The 5G-TRANSFORMER project (2017-2019) is an EU-funded project (H2020-ICT-2016-2 grant agreement 761536).
The 5G-CORAL project (2017-2019) is an EU-Taiwan project (H2020-ICT-2016-2 grant agreement 761586).Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Ioannis Stavrakakis.- Secretario: Pablo Serrano Yáñez-Mingot.- Vocal: Paul Horatiu Patra
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity
Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed.
A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition.
First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development.
An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists
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